Minimize Low-Order Harmonics in Low-Switching-Frequency Space-Vector-Modulated Current Source Converters With Minimum Harmonic Tracking Technique
Bibliographic record
Abstract
Gate turnoffs (GTOs) are usually used in high-power current source converters (CSCs), i.e., rectifiers and inverters. Space vector modulation (SVM) technique for CSC is established by dividing ac-side line current cycle into six sectors. Each sector is divided into a certain number of SV cycles. SV cycle is divided into three states: two active and one zero state. For low switching frequency as required by GTOs, the SVM technique generates fifth and seventh harmonics (HD5-7) in the CSC ac-side current. Minimal reduction in HD5-7was achieved with certain states sequence inside the SV cycle. Moderate reduction in HD5-7was obtained by calculating states on -times at once in the middle of each SV cycle. In this paper, larger reduction in HD5-7at CSC ac-side current is achieved by new techniques for calculating states on-times. First, two straightforward techniques are proposed. One calculates states on-times from SVM equations in the middle of each state on-time. The other calculates all states on -times when the state changes from one active state to the other. Both techniques are effective in reducing HD5-7. Then, minimum harmonics tracking (MHT) technique for calculating states on-times in SVM CSC is proposed. Tracking technique adjusts states on-times once per four ac-side line current cycles to give the least HD5-7. In CSC with a large overlap period, power factor affects HD5-7, so two-variables MHT technique for both active states on-times inside SV cycle is proposed to give the least HD5-7. Also, a variable perturbation tracking technique is proposed to reduce transient time with unperturbed steady-state operation. Finally, experimental investigations and obstacles are introduced.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".